DETAILED ACTION
This communication is a response to RCE for Application 18/768509 filed on 1/21/2026. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims:
Claims 1-10 are presented for examination.
Continued Examination under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114 was filed in this application after appeal to the Patent Trial and Appeal Board, but prior to a decision on the appeal. Since this application is eligible for continued examination under 37 CFR 1.114 and the fee set forth in 37 CFR 1.17(e) has been timely paid, the appeal has been withdrawn pursuant to 37 CFR 1.114 and prosecution in this application has been reopened pursuant to 37 CFR 1.114. Applicant’s submission filed on 1/21/2026 has been entered.
Response to Arguments
Applicants’ arguments in the amendment filed on 1/21/2026 regarding claim rejection under 35 USC § 103 with respect to Claims 1-10 are moot in view of the new ground of rejection.
35 USC 101
Applicants’ arguments in the amendment filed on 1/21/2026 regarding claim rejection under 35 USC § 101 with respect to Claims 1-10 have been considered and found unpersuasive.
Applicant argues that “searching backward” and “multisession analysis system” are not generic. Examiner disagrees because the limitations are generic and do not integrate the abstract idea of user’s web journey into practical application. For example, any process will enable a user to search a log file by specifying different dates [searching backward]. Also, wayback machine invented long ago does the same for an entire website. As to the “multisession analysis system,” the cited art shows that this technology existed long time ago which now is conventional and standard for computers.
Applicant then argues about 102/103 to support the allegation above. Examiner disagrees because the evaluation under 101, 102 and 103 are distinct. See below the updated rejection in view of the new amendments.
Applicant then provides summary of the claim language but still provides no support of why the claims are not abstract or how the “searching backward” and “multisession analysis system” integrates the identified abstract idea into practical application. In fact, the response provides no evidence of any of the allegations provided.
Thus, Examiner maintains his interpretation.
35 USC 103
Applicants’ arguments in the amendment filed on 1/21/2026 regarding claim rejection under 35 USC § 103 with respect to Claims 1-10 are moot in view of the new ground of rejection.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
A. Independent Claims 1 and 6:
Claims 1 and 6 relate to surveying/tracking user interactions and web journey. This is abstract idea because it falls under both buckets of the abstract concepts, the mental process and certain methods of organizing human activity like managing relationships or interactions between users like social activities. Examiner elaborate only on one bucket i.e., mental process where tracking user interactions and web journey is a concept performed in the human mind.
Claim 1 recites a user’s web journey that includes observation of data, evaluation of data and judgments of data. For example, receiving clickstream data, determine session characteristics of the data, determine characteristic to the session as an anchor session, then assigning and arranging other data into a multi session according to the anchor session characteristic. This idea is abstract because it falls under the mental process concept which includes concepts that performed in the human mind including an observation, evaluation, judgment, opinion. Note that recitation of generic components in a claim does not necessarily preclude that claim from reciting an abstract idea. Also, this is a text book abstract in view of electric power group case law.
Claim 1 recited receiving clickstream data that is a communication between a device and at least two servers. The limitation of receiving this data, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting (device, server, clickstream), nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the servers/clickstream or a device language, “receiving” in the context of this claim encompasses the user receiving the information about the user browsing history or clicks, etc.
Similarly, the limitations of determining session characteristics of this data based on association then selecting an anchor session, are directed to process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. For example, “determining”, “session characteristics”, and “anchor session” in the context of this claim encompasses the user marking, circling or pointing out a part of the user’s clicks on a piece of paper, connecting the circles with similar characteristics and compare it to the first subset of data that acts like an anchor session characteristic, making conclusion of relationship between the two sets of data.
Similarly, sequencing and determining types of the data is also a process that under its broadest reasonable interpretation covers the process in a human mind by determining a first or second set of data type and assign them a characteristic and then arrange the sets into two groups or multi session.
Moreover, stripping user’s identity information is a process that under its broadest reasonable interpretation convers the process in a human mind but for using “multisession analysis system.” Meaning that one can strip user’s identity by bolding or highlighting the data or using a generic process to remove it.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites a device, multisession analysis system and two device/servers and database as additional elements. These elements are not related to the steps of “determining, selecting, assigning or arranging.” The elements also are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of using a device to communicate with servers) such that it amounts no more than mere instructions to apply the exception using a generic computer. Further, the recitation of generic components in a claim does not necessarily preclude that claim from reciting an abstract idea.
Accordingly, the elements identified above do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements (device, content servers, database, multisession analysis system) are not used other than generically which amounts to no more than mere instructions to apply the exception using a generic computer. Mere instructions to apply an exception using a generic computer cannot provide an inventive concept. The claim is not patent eligible. Also, “searching backward,” & “multisession analysis system” are generic computer components performing its generic functions. Note that recitation of generic components in a claim does not necessarily preclude that claim from reciting an abstract idea.
Similar rationales apply to independent claim 6 because claim 6 is not substantially different from independent claim 1. Claim 6 recite a non-transitory computer-readable storage medium, executable instructions and a processor to perform the method recited in claim 1. The additional element of using a non-transitory computer-readable storage medium, executable instructions and processor amounts to no more than mere instructions to apply the exception using a generic computer. Mere instructions to apply an exception using a generic computer cannot provide an inventive concept. The claim is not patent eligible.
B. Dependent claims:
a. The dependent claims 2-5 and 7-10 are also rejected under 35 U.S.C 101 as being directed to non-statutory subject matter for the same reasons addressed above as the claims do not contain any additional element or combination of elements which amount to significantly more than the abstract idea itself.
Claims 2-3 and 7-8 recite additional elements of “purchase or sale,” are to further limiting on the claim which can be different type of data still covered under data that is performed by human mind or on paper.
Claims 4-5 and 9-10 recite additional elements of “association,” are to further limiting the association which can be performed by human mind or on paper.
The dependent claims 2-5 and 7-10 are not patent eligible because they do not contain any additional element or combination of elements which amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 calls for “a user web journey across multiple user devices,” preamble lines 1-2. Then in the same claim last two limitations, “…comprise a user web journey across multiple content servers.” It is not clear whether the user web journey across multiple user devices the same or not as “a user web journey across multiple content servers”?
It is not clear also whether claim 1 last line/limitation “the user web journey” refers to “a user web journey across multiple user devices,” preamble lines 1-2 or claim 1 limitation, “…comprise a user web journey across multiple content servers” (one before the last limitation).
Claim 6 include similar limitations, thus the same rationale applies.
Also Claim 6 limitation “…tracking the web journey of the user…” lacks antecedent basis.
Claims 2-5 and 7-10 are also rejected for depending on rejected base claim.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-10 are rejected under 35 U.S.C. 103 as being unpatentable over Placentra et al. (hereinafter Placentra) US 2015/0363823 A1 in view of Tessler US 2014/0089472 A1 and further in view of Peng US 2018/0240158 A1.
Regarding Claim 1, Placentra teaches a method for tracking a user web journey across multiple user devices (¶0028 & Figs. 2-4; a user tracking system 130 across plurality of user devices), the method comprising:
a multisession analysis system receiving a first data content comprising a first plurality of first data items (abstract; clickstream data from a plurality of clickstream events is received from each of a plurality of communication devices in response to a web page being loaded by each communication device which is the same as multisession analysis),
the first data content being transmitted over a network (Figs. 1-2 describe different networks), the first plurality of first data items comprising clickstream data generated by at least one computerized device (abstract; clickstream data from a plurality of clickstream events is received from each of a plurality of communication devices in response to a web page being loaded by each communication device) as the computerized device is utilized to access content from at least two different content servers (abstract & ¶0015-¶0016; accessing web pages which means that the web pages are served from web/content servers) and which has had user identity information removed (¶0042-¶0044 & Figs. 6-7; obvious because the system uses clickstream events and probability to determine a single user which means that data does not have the identity of the user instead it is a clickstream events from different locations. For example in step 250 the system uses threshold against the data to determine if it is for a single user or not),
and communicated between the at least one computerized device and the at least two different content servers (¶0015-¶0016; any given user may use several different devices at multiple locations to communicate with only content providers or publishes which means a plurality of content servers and plurality of content);
the multisession analysis system, upon receiving the first plurality of first data items, analyzing the first plurality of first data items and determining a first plurality of characteristics (Fig. 3 & ¶0033 & ¶0042; at step 130, potential matches between pairs of datastreams are determined. Two datastreams are said to match one another if they represent the same user who is using two different devices. The potential matches may be determined by comparing the features obtained from datastreams in any variety of ways, using, for example, a series of heuristics. For example, if features extracted from two datastreams indicate that at some point in time two devices shared the same IP address (indicating that they were on a common network), then the two datastreams may be potential matches. Likewise, if location information is available then two datastreams may be treated as potential matches if they were within a specified distance of one another (e.g., 5 miles) within a specified time interval (e.g. 20 minutes). Conversely, pairs of datastreams may be excluded as potential matches if their respective features indicate that they are unlikely to be associated with the same user. As a simple example, if there are two datastreams, each of which is associated with the same type of device (e.g., a mobile phone), the two datastreams are less likely to match than two datastreams that are each associated with different types of devices (since a given user is less likely to have two devices of the same type)),
each of the first plurality of characteristics characterizing at least one first data item of the first plurality of first data items (Fig. 5 & ¶0042-¶0044; Next, at step 220, the clickstream events in the two datastreams D1 and D2 are combined into a single datastream chain. Specifically, the datastreams D1 and D2 are combined on a segment-by-segment basis. As shown in FIG. 7 the clickstream events in datastreams D1 and D2 obtained from two potentially matching communication devices are distributed along a single chain. The segments associated with the chain (which are formed from the combination of the individual segments of the two datastreams) are referred to herein as an observation. Accordingly, the chain comprises a series of observations that are respectively co-extensive with the duration of the segments in the individual datastreams D1 and D2);
wherein the at least one second data item is from a different content server than the first data item (¶0015-¶0016; any given user may use several different devices at multiple locations to communicate with only content providers or publishes which means a plurality of content servers and plurality of content. Also see Fig. 5 & abstract; determining a likelihood that two or more communication devices are associated with a common user, clickstream data from a plurality of clickstream events is received from each of a plurality of communication devices in response to a web page being loaded by each of the communication devices. Web page loaded by each communication devices implies different content servers), wherein both content servers are being used by the same user (Fig. 5 associating clickstream data with a single device), to form a sequence of at least two data items associated with the anchor characteristic (¶0019, ¶0028 & ¶0032; one data stream representing the sequence of clickstream events for communication device 1 and the other data stream representing the sequence of clickstream events for communication device 2. Fig. 4 also represent sequence of clickstream events for two different communication devices where in Fig. 5, the system determines if those devices are for a single user or not. Note also that segment analysis for data is similar to anchor characteristic. See Fig. 5 & ¶0042-¶0044);
wherein the at least two data items comprise clickstream data communicated between the at least one computerized device and at least two different content servers identifying the at least two data items from different websites and/or different computerized devices as being associated with the same user (¶0015-¶0016, ¶0042 & fig. 7; any given user may use several different devices at multiple locations to communicate with only content providers or publishes which means a plurality of content servers and plurality of content. Also see Fig. 4 represent sequence of clickstream events for two different communication devices where in Fig. 5, the system determines if those devices are for a single user or not. ¶0019 & ¶0033; two datasreams are said to match one another if they represent the same user who is using two different devices);
Placentra does not expressly teach the multisession analysis system determining a characteristic of the first plurality of characteristics as an anchor characteristic, the multisession analysis system determining a first data item associated with the anchor characteristic and wherein the at least one second data item precedes in time the first data item associated with the anchor characteristic.
Tessler teaches the multisession analysis system determining a characteristic of the first plurality of characteristics as an anchor characteristic (¶0055; a segment may be defined such as "Hockey" [anchor characteristic], and this segment will include a list of phrases that give meaning or depth to the topic (e.g. off-side, puck, Stanley Cup, NHL, rookie season, skate, skated, body check, etc. The present invention then uses a variety of mechanisms to measure the interaction of the user with these phrases. For example, the computer system tracks how many times a user has viewed these words across multiple sites, by detecting and logging such interactions as (mouseing over, selecting, clicking hyperlinks with those any of the words in it, etc.). This information, in one implementation, is sent to the central server and is "scored" to represent how much the user is interested in "hockey". Interest may be measured for example as a value between 0-1 (a percentage));
the multisession analysis system determining a first data item associated with the anchor characteristic (¶0055; a segment may be defined such as "Hockey" [anchor characteristic], and this segment will include a list of phrases that give meaning or depth to the topic (e.g. off-side, puck, Stanley Cup, NHL, rookie season, skate, skated, body check, etc. The present invention then uses a variety of mechanisms to measure the interaction of the user with these phrases. For example, the computer system tracks how many times a user has viewed these words across multiple sites [first data item], by detecting and logging such interactions as (mouseing over, selecting, clicking hyperlinks with those any of the words in it, etc.). This information, in one implementation, is sent to the central server and is "scored" to represent how much the user is interested in "hockey". Interest may be measured for example as a value between 0-1 (a percentage)); and
wherein the at least one second data item precedes in time the first data item associated with the anchor characteristic (¶0058; We can now compare the total keywords viewed and exposure time in segment and compare it to another. You compare recency of data; i.e. last week vs. this week. Or how someone's interest in a segment(s) changes over time (daily, weekly, monthly). You can see how sub-segments compare against each other or how they affect the overall score within a segment. E.g. Hockey and its words are all a sub-segment of "Sports". You could compare someone relative interests in Hockey vs. Tennis. Or determine that someone is a rabid hockey fan but not a fan of any other sports);
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed limitation to incorporate the teachings of Tessler into the system of Placentra in order for capturing knowledge and interests of a user by conducting semantic knowledge capture, utilizing a real-time, or near real-time, viewport analysis (abstract). Utilizing such teachings enable the system to track the occurrences of the one or more objects being viewable through the viewpoint in a tracking array, thereby to analyze the user's knowledge and interests in the one or more objects (abstract).
Placentra in view of Tessler does not expressly teach the multisession analysis system searching backward in time to find at least one second data item of the first plurality of first data items, wherein the at least two data items comprise a user web journey across multiple content servers, and ending with the anchor characteristic, storing the user web journey in a multisession database.
Peng the multisession analysis system searching backward in time to find at least one second data item of the first plurality of first data items (¶0030; when clickstream data is converted into categorized structured data, this implies making the data content grouping searchable based on particular categories/characteristics),
wherein the at least two data items comprise a user web journey across multiple content servers, and ending with the anchor characteristic (Fig. 3c & ¶0030; data analytics program analyzes click events and categorize it. For example, Fig. 3c show user Jack and preferred dates [similar to anchor characteristic] then the system generates and categorize all the click events for the preferred dates and provide it as customer profile generated or built based on enriched data models, according to an embodiment of the present invention. These profiles may be used to observe and compare the interests and information of each individual in order to assist in assessing their needs, their spending powers, their probable budget etc.); and
storing the user web journey in a multisession database (¶0012; storing the information using a data warehousing module, processing the information using a Data-as-a-Service (DaaS) module to provide to personalized recommendations to the customers in real-time, wherein the Data-as-a-Service (DaaS) module performs the steps of profiling the customers using a customer profiling module based on the plurality of information related to the interactions and interests of the customers and a plurality of historical behavior of the customers retrieved from the data warehousing module).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed limitation to incorporate the teachings of Peng into the system of Placentra in view of Tessler in order to convert stored clickstream data into user interest categories (¶0009). Utilizing such teachings allows the present method to track, learn and profile customers at scale and capture behavior at scale, track and store behavior and engagement in real-time across millions of visitors (¶0031).
Regarding Claim 2, Placentra in view of Tessler and further in view of Peng teaches the method according to claim 1, Tessler further teaches wherein the anchor characteristic is associated with at least one of a purchase of an article and a sale of an article (¶0055 & ¶0273; obvious because it is within the web clicks that is being gathered and analyzed. Also see Placentra as mapped to claim 1 for anchor characteristic).
Regarding Claim 3, Placentra in view of Tessler and further in view of Peng teaches the method according to claim 1, Placentra further teaches wherein the at least two data items comprise clickstream data communicated between the at least two computerized devices and at least two content servers (Fig. 2 illustrate two users [two devices] communicate with tow websites [two content servers]).
Regarding Claim 4, Placentra in view of Tessler and further in view of Peng teaches the method according to claim 3, Placentra further teaches additionally comprising: the multisession analysis system determining which of the at least two computerized devices is associated with the first data item associated with the anchor characteristic (Figs. 2-5 data is identified to determine which device is associated with anchor characteristic. For example, in Fig. 3 & ¶0033 & ¶0042; at step 130, potential matches between pairs of datastreams are determined. Two datastreams are said to match one another if they represent the same user who is using two different devices. The potential matches may be determined by comparing the features obtained from datastreams in any variety of ways, using, for example, a series of heuristics. For example, if features extracted from two datastreams indicate that at some point in time two devices shared the same IP address (indicating that they were on a common network), then the two datastreams may be potential matches. Likewise, if location information is available then two datastreams may be treated as potential matches if they were within a specified distance of one another (e.g., 5 miles) within a specified time interval (e.g. 20 minutes). Conversely, pairs of datastreams may be excluded as potential matches if their respective features indicate that they are unlikely to be associated with the same user. As a simple example, if there are two datastreams, each of which is associated with the same type of device (e.g., a mobile phone), the two datastreams are less likely to match than two datastreams that are each associated with different types of devices (since a given user is less likely to have two devices of the same type)).
Regarding Claim 5, Placentra in view of Tessler and further in view of Peng teaches the method according to claim 1, Placentra further teaches additionally comprising: the multisession analysis system associating a characteristic with each of the at least two data items (Fig. 3 & ¶0033 & ¶0042; at step 130, potential matches between pairs of datastreams are determined. Two datastreams are said to match one another if they represent the same user who is using two different devices. The potential matches may be determined by comparing the features obtained from datastreams in any variety of ways, using, for example, a series of heuristics. For example, if features extracted from two datastreams indicate that at some point in time two devices shared the same IP address (indicating that they were on a common network), then the two datastreams may be potential matches. Likewise, if location information is available then two datastreams may be treated as potential matches if they were within a specified distance of one another (e.g., 5 miles) within a specified time interval (e.g. 20 minutes). Conversely, pairs of datastreams may be excluded as potential matches if their respective features indicate that they are unlikely to be associated with the same user. As a simple example, if there are two datastreams, each of which is associated with the same type of device (e.g., a mobile phone), the two datastreams are less likely to match than two datastreams that are each associated with different types of devices (since a given user is less likely to have two devices of the same type));
Placentra does not expressly teach forming the characteristics of the at least two data items into a semantic network & associating the semantic network with the anchor characteristic.
Tessler further teaches forming the characteristics of the at least two data items into a semantic network (¶0011 & Fig. 2c; initializing a semantic knowledge capture session for the user, using one or more computer processors);
associating the semantic network with the anchor characteristic (¶0114-¶0121; semantic knowledge session of the user. Note that anchor characteristic is disclosed in ¶0055; a segment may be defined such as "Hockey" [anchor characteristic], and this segment will include a list of phrases that give meaning or depth to the topic (e.g. off-side, puck, Stanley Cup, NHL, rookie season, skate, skated, body check, etc. The present invention then uses a variety of mechanisms to measure the interaction of the user with these phrases. For example, the computer system tracks how many times a user has viewed these words across multiple sites [first data item], by detecting and logging such interactions as (mouseing over, selecting, clicking hyperlinks with those any of the words in it, etc.). This information, in one implementation, is sent to the central server and is "scored" to represent how much the user is interested in "hockey". Interest may be measured for example as a value between 0-1 (a percentage)).
See above reasons for combining the cited art.
Claims 6-10 are substantially similar to the above claims, thus the same rationale applies.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAHRAN ABU ROUMI whose telephone number is (469)295-9170. The examiner can normally be reached Monday-Thursday 6AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emmanuel Moise can be reached at 571-272-3865. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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MAHRAN ABU ROUMI
Primary Examiner
Art Unit 2455
/MAHRAN Y ABU ROUMI/Primary Examiner, Art Unit 2455